This paper proposes a spatial-temporal autoregressive model for the mortality surface, where mortality rates of each age depend on the historical values of itself (temporality) and the neighbouring ages (spatiality). The mortality dynamics is formulated as a large, first order vector autoregressive model which encompasses standard factor models such as the Lee and Carter (1992) model. Sparsity and smoothness constraints are then introduced, based on the idea that the nearer the two ages, the more important the dependence between mortalities at these ages. Our model has several novelties. First, it ensures that in the long-run, mortality rates at different ages do not diverge. Second, it provides a natural explanation of the so-called cohort effect without identifiability difficulties. Third, the model is easily extended to the multiple-population case in a coherent way. Finally, the model is associated with a closed form, non-parametric estimation method: the penalized least square, which ensures spatial smoothness of the age-dependent parameters. Using US and UK mortality data, we find that our model produces reasonable projected mortality profile in the long-run, as well as satisfying short-run out-of-sample forecast performance.

Airbnb has been increasingly gaining popularity since 2008 due to its low prices and direct interactions with the local community. This paper employed a general linear model (GLM) and a geographically weighted regression (GWR) model to identify the key factors affecting Airbnb listing prices using data sets of 794 samples of Airbnb listings of business units in Metro Nashville, Tennessee. The results showed that the GWR model performs better than the GLM in terms of accuracy and affected variable selections. Statistically significant differences varied across regions in Metro Nashville. The coefficients illustrate a decreasing trend while there is an increase in the distance from the listed units to the convention center, which indicates that Airbnb listing prices are more sensitive to the distance from the convention center in the central area than in other areas. These findings can also provide implications for stakeholders such as Airbnb hosts to gain a better understanding of the market situation and formulate a suitable pricing strategy.

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